Gradual Power Wake-Up Mechanism

ABSTRACT

Apparatuses and methods of a gradual power wake-up mechanism are disclosed. In one embodiment, a method of activating a device based on detection of a fingerprint image may include monitoring a first metric level of a first set of regions of the fingerprint image, determining a second metric level of a second set of regions of the fingerprint image in response to the first metric level exceeding a first threshold, and activating the device based on the second metric level of the second set of regions of the fingerprint image.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. patent application No.62/217,538, “Gradual Power Wake Up Mechanism,” filed Sep. 11, 2015,which is assigned to the assignee hereof. The aforementioned UnitedStates patent application is hereby incorporated by reference in itsentirety.

FIELD

The present disclosure relates to the field of wireless communications.In particular, the present disclosure relates to wake-up mechanisms formobile devices.

BACKGROUND

Conventional mobile devices may not be able to detect whether the devicemight be used or not in the near future until a user depresses an“on/off” button or touches a portion of the display. While in thisuncertain state, conventional mobile devices may stay active or maybecome active periodically to perform a number of background tasks anddata synchronizations in anticipation that the mobile device might beused. Such background tasks and data synchronizations may unnecessarilyconsume limited battery resources or consume communication/processingbandwidth. Therefore, it would be beneficial to use a wake-up mechanismthat may conserve limited battery resources, conservecommunication/processing bandwidth, or both, and/or in some other mannerimprove the operation of a mobile device.

SUMMARY

The present disclosure relates to apparatuses and methods of a gradualpower wake-up mechanism. In one embodiment, a method of activating adevice based on detection of a fingerprint image may include monitoringa first metric level of a first set of regions of the fingerprint image,determining a second metric level of a second set of regions of thefingerprint image in response to the first metric level exceeding afirst threshold, and activating the device based on the second metriclevel of the second set of regions of the fingerprint image. The firstmetric level and the second metric level may represent at least one ofan acoustic energy level, an acoustic loading level, a spatialfrequency, a cross-correlation value, or an image quality value.

According to aspects of the present disclosure, the method of monitoringthe first metric level of the first set of regions of the fingerprintimage may include receiving first sampled data from the first set ofregions of the fingerprint image at a first sampling rate anddetermining the first metric level for indicating an initial predictionof a presence of a finger using the first sampled data. The method mayfurther include monitoring the first metric level of the first set ofregions of the fingerprint image in response to the first metric levelbeing less than or equal to the first threshold.

The method of determining the second metric level of the second set ofregions of the fingerprint image may include receiving second sampleddata from the second set of regions of the fingerprint image anddetermining the second metric level for indicating a finer prediction ofa presence of a finger using the second sampled data. In someimplementations, the second set of regions may include a portion of anactive area of an ultrasonic sensor or the entire active area of theultrasonic sensor.

The method of activating the device based on the second metric level ofthe second set of regions of the fingerprint image may includedetermining a presence of a finger in response to the second metriclevel exceeding a second threshold and activating the device in responseto the presence of the finger. The method may further include monitoringthe first metric level of the first set of regions of the fingerprintimage in response to the second metric level being less than or equal tothe second threshold.

The method of determining the first metric level of the first set ofregions of the fingerprint image may include determining a change inforeground based on a presence of the fingerprint image, performing abackground estimation for the first set of regions of the fingerprintimage, and determining the first metric level of the first set ofregions based on differences between the change in foreground and thebackground estimation for the first set of regions of the fingerprintimage.

The method of determining the change in foreground may include receivingfirst sampled foreground data in the first set of sampled data where thefirst sampled foreground data is collected with an ultrasonictransmitter in an enabled state, receiving second sampled foregrounddata in the first set of sampled data where the second sampledforeground data is collected with the ultrasonic transmitter in adisabled state, and computing the change in foreground for the first setof regions of the fingerprint image as a difference between the firstsampled foreground data and the second sampled foreground data.

The method of performing the background estimation may includedetermining an updated acquisition time delay and an updated ultrasonictransmitter frequency in accordance with a variation of a currenttemperature from a reference temperature from which an initialbackground estimation and an initial ultrasonic transmitter frequencyare determined, acquiring background image information based on theupdated acquisition time delay and the updated ultrasonic transmitterfrequency, and computing the background estimation using the backgroundimage information.

The method may further include at least one of reducing background noisebased on autocorrelation of the pixels in the first set of regions,reducing sensor artifacts by removing quiescent values in the firstsampled data, or a combination thereof.

The method may further include receiving third sampled data from a thirdset of regions of the fingerprint image, determining a third metriclevel of the third set of regions for indicating an enhanced predictionof a presence of a finger using the third sampled data, and activatingthe device based on a combination of the second metric level and thethird metric level, where the third set of regions includes more pixelsthan the second set of regions and where the second set of regionsincludes more pixels than the first set of regions.

In some implementations, a device may include a sensor having aplurality of sensor pixels configured to sense a fingerprint image, amemory configured to store the fingerprint image, and a controller. Thecontroller may be configured to monitor a first metric level of a firstset of regions of the fingerprint image, determine a second metric levelof a second set of regions of the fingerprint image in response to thefirst metric level exceeding a first threshold where the second set ofregions includes more pixels than the first set of regions, and activatethe device based on the second metric level of the second set of regionsof the fingerprint image.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned features and advantages of the disclosure, as well asadditional features and advantages thereof, will be more clearlyunderstandable after reading detailed descriptions of embodiments of thedisclosure in conjunction with the non-limiting and non-exhaustiveaspects of following drawings. Like numbers are used throughout thefigures.

FIG. 1A illustrates an exemplary block diagram of a mobile deviceaccording to aspects of the present disclosure.

FIG. 1B illustrates an exemplary implementation of the sensor subsystemof the mobile device of FIG. 1A according to aspects of the presentdisclosure.

FIG. 2 illustrates an example of a gradual power wake-up mechanismaccording to aspects of the present disclosure.

FIG. 3 illustrates exemplary sensor implementations of the gradual powerwake-up mechanism of FIG. 2 according to aspects of the presentdisclosure.

FIG. 4A illustrates an example of power consumption over time in themethod of FIG. 2 according to aspects of the present disclosure.

FIG. 4B illustrates another example of power consumption over time inthe method of FIG. 2 according to aspects of the present disclosure.

FIG. 4C illustrates exemplary implementation results of the gradualpower wake-up mechanism of FIG. 2 according to aspects of the presentdisclosure.

FIG. 5 illustrates a method of activating a device based on detection ofa fingerprint image according to aspects of the present disclosure.

FIG. 6A illustrates a method of monitoring a first metric level of afirst set of regions of the fingerprint image of FIG. 5 according toaspects of the present disclosure.

FIG. 6B illustrates a method of determining a second metric level of asecond set of regions of the fingerprint image of FIG. 5 according toaspects of the present disclosure.

FIG. 6C illustrates a method of activating the device based on thesecond metric level of the second set of regions of the fingerprintimage of FIG. 5 according to aspects of the present disclosure.

FIG. 6D illustrates an exemplary method of determining a metric levelfor a set of regions of the fingerprint image according to aspects ofthe present disclosure.

FIG. 6E illustrates an exemplary method of determining the change inforeground for a set of regions of the fingerprint image according toaspects of the present disclosure.

FIG. 6F illustrates an exemplary method of performing backgroundestimation according to aspects of the present disclosure.

FIG. 7 illustrates an exemplary block diagram of a device that may beconfigured to implement a gradual power wake-up mechanism according toaspects of the present disclosure.

FIGS. 8A-8C illustrate an example of an ultrasonic sensor according toaspects of the present disclosure.

FIG. 9A illustrates an example of a four-by-four array of sensor pixelsfor an ultrasonic sensor array according to aspects of the presentdisclosure.

FIG. 9B illustrates an example of a high-level block diagram of anultrasonic sensor system according to aspects of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Embodiments of a gradual power wake-up mechanism are disclosed. Thefollowing descriptions are presented to enable a person skilled in theart to make and use the disclosure. Descriptions of specific embodimentsand applications are provided only as examples. Various modificationsand combinations of the examples described herein may be readilyapparent to those skilled in the art, and the general principles definedherein may be applied to other examples and applications withoutdeparting from the scope of the disclosure. Thus, the present disclosureis not intended to be limited to the examples described and shown, butis to be accorded the scope consistent with the principles and featuresdisclosed herein. The word “exemplary” or “example” is used herein tomean “serving as an example, instance, or illustration.” Any aspect orembodiment described herein as “exemplary” or as an “example” is notnecessarily to be construed as preferred or advantageous over otheraspects or embodiments.

FIG. 1A illustrates an exemplary block diagram of a mobile deviceaccording to aspects of the present disclosure. In the example shown inFIG. 1A, a mobile device 100 may include wireless connection module 102,controller 104, sensor subsystem 106, memory 110 and applications module108. The mobile device 100 may optionally include multimedia subsystem112, speaker(s) and microphone(s) 114, and display 116. In someimplementations, the wireless connection module 102 may be configured tosupport WiFi and/or Bluetooth in a wireless local area network (LAN) orwireless personal area network (PAN). The controller 104 may include oneor more processors, software, hardware, and firmware to implementvarious functions described herein. For example, the controller 104 maybe configured to implement functions of the mobile device 100 asdescribed in FIG. 2 to FIG. 6. The sensor subsystem 106 may beconfigured to sense and process various sensor input data and producesensor output data to the controller 104. The applications module 108may include a battery charging circuit and power manager, oscillators,phase lock loops, clock generators and timers.

In some implementations, the sensor subsystem 106 may be configured tosense and detect a user's finger in low power conditions. For example,the sensor subsystem 106 may be configured to include a sensor having aplurality of sensor pixels that may be configured as a low-powerdetector (not shown), such as a 270-pixel detector configuration, todetermine energy levels of certain areas of the fingerprint image and tomake an initial prediction of the presence of a finger. In someimplementations, the plurality of sensor pixels may be configured as anintermediate-level detector, such as a 1782-pixel detectorconfiguration, to determine energy levels of certain areas of thefingerprint image that may include the sensor pixels of the low-powerdetector configuration. The intermediate-level detector may beconfigured to make a finer prediction of the presence of a finger. Insome implementations, the plurality of sensor pixels may be configuredas an enhanced detector, where all of the pixels in the sensor areutilized to determine the presence of a finger using the methodsdescribed herein. The controller 104 may be engaged to work with thelow-power detector configuration, the intermediate-level detectorconfiguration, and/or the enhanced detector configuration to determinethe presence of a finger. The controller 104 and associated componentsof the sensor subsystem 106 typically consume more power and requiremore signal processing resources when engaged to work with thefull-sensor detector than the low-power detector configuration or theintermediate-level detector configuration operated by the sensorsubsystem 106.

In certain embodiments, mobile device 100 may include a wirelesstransceiver that is capable of transmitting and receiving wirelesssignals via a wireless antenna over a wireless communication network.Some embodiments may include multiple wireless transceivers and wirelessantennas to enable transmitting and/or receiving signals according tocorresponding multiple wireless communication standards such as, forexample, versions of IEEE Std. 802.11, CDMA, WCDMA, LTE, UMTS, GSM,AMPS, Zigbee and Bluetooth, etc.

Wireless connection module 102 may include an SPS receiver capable ofreceiving and acquiring SPS signals via an SPS antenna. The SPS receivermay also process, in whole or in part, acquired SPS signals forestimating a location of mobile device 100. In some embodiments,controller 104 and memory 110 may also be utilized to process acquiredSPS signals, in whole or in part, and/or calculate an estimated locationof mobile device 100, in conjunction with the SPS receiver. SPS or othersignals for use in performing positioning operations may be stored inmemory 110 or registers (not shown).

In various embodiments, controller 104 may be configured to execute oneor more machine-readable instructions stored in memory 110 such as on acomputer-readable storage medium, such as RAM, ROM, FLASH, or discdrive, just to name a few examples. The one or more instructions may beexecutable by one or more processors, specialized processors, or DSPs.Memory 110 may include a non-transitory processor-readable memory and/ora computer-readable memory that stores software code (programming code,instructions, etc.) that are executable by the processors and/or DSPs toperform functions described herein. Controller 104 may executeinstructions to perform one or more aspects of processes/methodsdiscussed below in connection with FIG. 2 to FIG. 6.

In some implementations, a user interface may include any one of severaldevices such as, for example, multimedia subsystem 112, speakers andmicrophones 114, display 116, etc. In a particular implementation, theuser interface may enable a user to interact with one or moreapplications hosted on mobile device 100. For example, devices may storeanalog or digital signals in memory 110 to be further processed bycontroller 104 in response to an action from a user. Similarly,applications hosted on mobile device 100 may store analog or digitalsignals on memory 110 to present an output signal to a user.

Mobile device 100 may also include a camera for capturing still ormoving imagery. The camera may include, for example, an imaging sensor(e.g., charge coupled device or CMOS imager), lens, analog to digitalcircuitry, frame buffers, etc. In some implementations, additionalprocessing, conditioning, encoding or compression of signalsrepresenting captured images may be performed by controller 104.Alternatively, a video processor may perform conditioning, encoding,compression or manipulation of signals representing captured images.Additionally, the video processor may decode/decompress stored imagedata for presentation on display 116 of mobile device 100.

FIG. 1B illustrates an exemplary implementation of the sensor subsystemof the mobile device of FIG. 1A according to aspects of the presentdisclosure. Sensor subsystem 106 may generate analog or digital signalsthat may be stored in memory 110 and processed by controller 104 insupport of one or more applications such as, for example, applicationsrelate to activating a device based on detection of a fingerprint image.

As shown in FIG. 1B, the sensor subsystem 106 may include one or moresensor input devices 122, sensor processing module 124, and one or moresensor output devices 126. The one or more sensor input devices 122 mayinclude the low-power (fingerprint image) detector configuration and theintermediate-level (fingerprint image) detector configuration asdescribed above in association with FIG. 1A. The one or more sensorinput devices 122 may also include one or more of keys and buttons,ultrasonic sensors, temperature and moisture sensors, microphones,ultrasound microphone arrays, photo detectors, image sensors, touchsensors, pressure sensors, chemical sensors, gyroscopes, accelerometers,magnetometers, GPS, and compass. The sensor processing module 124 may beconfigured to perform one or more of the following functions, includingbut not limited to: input sensor selection and control, synchronizationand timing control, signal processing, sensor platform performanceestimation, sensor optimization, sensor fusion, and output sensor/deviceselection and control. The one or more sensor output devices 126 mayproduce one or more ultrasonic, voice, visual, biometric, nearness,presence, pressure, stability, vibration, location, orientation,heading, kinetics and chemical signals. The sensor subsystem 106 may beconfigured to implement functions of activating a device based ondetection of a fingerprint image as described in FIG. 2 to FIG. 6.

The sensor processing module 124 may be configured to process sensorinput data from the one or more sensor input devices 122, and produceoutput commands or signals to the one or more sensor output devices 126and/or to the one or more optional active sensor output devices.According to aspects of the present disclosure, direct user inputs maybe used to predictably manipulate power control behavior. In someembodiments, a mobile device may be configured to accept user commands(via direct, voice/aural and/or visual inputs) and be configured tosense a multitude of use, use environment and use contexts.

In some implementations, the sensor processing module 124 may include anapplication-specific integrated circuit (ASIC) that includes circuitrysuch as a plurality of voltage regulators for generating a plurality ofpower supply voltages; memory, finite-state machines, level shifters andother associated circuitry for generating control signals to anultrasonic fingerprint sensor having a plurality of sensor pixels;circuitry for generating transmitter excitation signals, range-gatedelay signals, diode bias signals and receiver bias signals to theultrasonic sensor; circuitry for analog signal conditioning,analog-to-digital conversion and digital processing of the receivedpixel output signals from the ultrasonic sensor; and interface circuitryfor sending digital output signals to an applications processor of amobile device. The applications processor may execute the methodsdescribed in this disclosure. For purposes of minimizing powerconsumption, the methods may be executed on an isolated low-power islandof the applications processor so that power need not be supplied to theentire applications processor when in sleep mode. In a low-power sleepmode, the applications processor may command the ASIC to access andacquire output signals from a limited number of sensor pixels, andsubsequently the applications processor may process the digitizedinformation from the ASIC to make a determination on finger presence.

In other implementations, in addition to the ASIC circuitry described inthe prior paragraph, the ASIC may also include a microcontroller toautonomously execute one or more initial stages of the wake-up algorithmlocally on the ASIC. If the initial prediction of the presence of afinger is positive, the microcontroller in the ASIC may communicate viaan interrupt mechanism with the applications processor and wake up aportion or more of the applications processor to make an intermediate orenhanced determination of the presence of the finger. For overalllow-power operation, it may be desirable that the microcontroller makedeterminations before requesting and enlisting the processing resourcesof the applications processor and other components of the mobile device.In some implementations, the intermediate and/or enhanced determinationof the presence of a finger may be performed by the applicationsprocessor, in part by accessing and acquiring output signals from alarger set of sensor pixels, which may include the entire active area ofthe sensor. If the presence of a finger has been detected, fingerprintimage information may be acquired and used for matching with enrolledfingerprint information and authentication of a candidate user, alongwith other functions of the applications processor.

In yet other implementations, in addition to the microcontroller andASIC circuitry noted above, the ASIC may also include the ultrasonicsensor pixel array and associated circuitry such as row-drivers andcolumn-gate drivers to scan the pixels. In these implementations, theASIC may execute the functions of sensing the sensor pixel outputsignals in addition to the functions of finger presence detection andother functions described herein.

FIG. 2 illustrates a gradual power wake-up mechanism according toaspects of the present disclosure. In the exemplary gradual powerwake-up mechanism shown in FIG. 2, in block 202, a device is configuredto monitor a first set of regions at a first sampling rate, for exampleusing the low-power detector configuration described in FIG. 1A. In someimplementations, the first sampling rate may be 5 Hz, 10 Hz, 20 Hz, 100Hz or other sampling rate, depending on the size of the fingerprintimage being monitored, resolution, power consumption, and/or otherfactors. In block 204, the device may be configured to estimate a firstmetric level of the first set of regions of the fingerprint image. Insome implementations, the first metric level is a measurement ofreflected acoustic energy being received at a piezoelectric receiverfrom the first set of regions. The first metric level may be used toindicate an initial prediction of whether an object or a user's fingerhas been detected. In other implementations, other metrics and theirassociated metric levels may be employed to detect the object or theuser's finger, such as fingerprint features (e.g., ridges and valleys),presence of certain spatial frequencies, acoustic impedance, etc.

In block 206, the device may be configured to determine, for example bycontroller 104 and/or sensor processing module 124, whether the firstmetric level exceeds a first threshold. If the first metric levelexceeds the first threshold (206_Yes), the method may move to block 208.Alternatively, if the first metric level does not exceed the firstthreshold (206_No), the method may move back to block 202, where theprocess of monitoring a first metric level of a first set of regions ofthe fingerprint image is repeated.

In block 208, the device may be configured to monitor a second set ofregions of the fingerprint image at a second sampling rate, for exampleusing the intermediate-level detector configuration as described in FIG.1A. In some implementations, the second sampling rate may be just onceor at a frequency depending on the size of the fingerprint image beingmonitored, resolution, power consumption, and/or other factors. In someimplementations, the second sampling rate may be equal to or faster thanthe first sampling rate. In block 210, the device may be configured toestimate a second metric level of the second set of regions of thefingerprint image. In some implementations, the second metric level is ameasurement of reflected acoustic energy being received at apiezoelectric receiver from the second set of regions. The second metriclevel may be used to indicate a finer prediction of whether an object ora user's finger has been detected. In other embodiments, other metricsand their associated metric levels may be employed to detect the objector the user's finger, such as fingerprint features like ridges andvalleys, presence of certain spatial frequencies, acoustic impedance,etc.

In block 212, the device may be configured to determine, for example bycontroller 104 and/or sensor processing module 124, whether the secondmetric level exceeds a second threshold. If the second metric levelexceeds the second threshold (212_Yes), the method moves to block 214.Alternatively, if the second metric level does not exceed the secondthreshold (212_No), the method may move back to block 202, where theprocess of monitoring a first metric level of a first set of regions ofthe fingerprint image is repeated.

In some embodiments, blocks 208, 210 and 212 may be bypassed, indicatedby the dashed line from block 206 to block 214, in response to the firstmetric level exceeding a first threshold.

In block 214, the controller 104 and/or sensor processing module 124 maydetermine whether a user's finger has been detected in response to thesecond metric level exceeding a second threshold, and send a signal toactivate the device in response to the user's finger being detected.Alternatively or additionally, the sensor processing module 124 mayfurther analyze the fingerprint image of the entire active sensor areato determine whether the user's finger has been detected and activatethe device in response to the user's finger being detected.

According to aspects of the present disclosure, sampled data may becollected from a third set of regions of the fingerprint image. In someexemplary implementations, controller 104 and/or sensor processingmodule 124 may be configured to receive third sampled data from a thirdset of regions of the fingerprint image, and determine a third metriclevel of the third set of regions for indicating an enhanced predictionof a presence of a finger using the third sampled data, and activatingthe device based on a combination of the second metric level and thethird metric level, where the second set of regions includes more pixelsthan the first set of regions, and the third set of regions includesmore pixels than the second set of regions. In one approach, the thirdset of regions may include the entire sensing region (e.g., entireactive area) of the fingerprint image, such as the active area of anultrasonic sensor array. For example, the first set of regions may be a270-pixel detector configuration, the second set may be a 1782-pixeldetector configuration, and the third set may be an entire active areaof a 14,400-pixel detector. In this implementation, the mobile devicemay be taken out of a sleep mode and activated (e.g., woken up), whenthreshold values are exceeded for each of the 270-pixel detectorconfiguration, the 1782-pixel detector configuration, and the14,400-pixel detector (entire active area) configuration.

FIG. 3 illustrates exemplary implementations of the gradual powerwake-up mechanism of FIG. 2 according to aspects of the presentdisclosure. As shown in FIG. 3, block 302 represents an exemplaryfingerprint image being monitored in block 202 of FIG. 2. Lines 304,306, 308, 310, etc. represent the first set of regions of thefingerprint image being sampled at the first sampling rate. As notedabove, at this stage, a low-power detector configuration, such as a270-pixel detector configuration, the controller 104 and/or the sensorprocessing module 124 may be used to estimate the first metric level andcompare the first metric level to the first threshold as described inblocks 202 to 206 of FIG. 2.

Similarly, block 312 represents the fingerprint image being monitored inblock 208 of FIG. 2. Clusters 314, 316, and 318 represent the second setof regions of the fingerprint image being sampled at the second samplingrate. At this stage, an intermediate-level detector configuration, suchas a 1782-pixel detector configuration, the controller 104 and/or thesensor processing module 124 may be used to estimate the second metriclevel and compare the second metric level to the second threshold asdescribed in blocks 208 to 212 of FIG. 2.

In the event the second metric level exceeds the second threshold, thepresence of a user's finger may be detected and a signal may be sent bythe controller 104 and/or the sensor processing module 124 of the sensorsubsystem 106 to turn on the device 100. After the device 100 has beenturned on, block 322 represents the fingerprint image being monitored.In some embodiments, block 312 may be bypassed, indicated by a dashedline from block 302 to block 322, in response to the first metric levelexceeding a first threshold, as described in blocks 202 to 206 of FIG.2. Block 324 represents a full-sensor detector configuration, such as a14,400-pixel detector configuration, that may be used to monitorsubsequent operations, for example subsequent uses of the device 100. Insome implementations, the 14,400-pixel detector configuration representsthe entire active area of a fingerprint sensor. In some implementations,the fingerprint sensor may serve as a home button or other type ofbutton in device 100.

FIG. 4A illustrates an example of power consumption over time forexecuting portions of the method of FIG. 2 according to aspects of thepresent disclosure. In this example, in the standby mode, the powerconsumed by the sensor subsystem 106 is represented by numeral 402.Power is consumed to acquire samples in the first set of regions at thefirst sampling rate, to estimate an energy level, and to compare theestimated energy level to a threshold. In this mode, only a small set ofthe pixels of the fingerprint image are sampled and the number ofcomputations may be significantly reduced. Both factors contribute toreducing the power consumption in the standby mode.

At time 406, assuming a finger has been preliminarily detected, thedevice may continue to perform the gradual power wake-up mechanism asdescribed in FIG. 2. Numeral 404 represents the power consumed forblocks 202 to 206 of FIG. 2. Assuming the first metric level exceeds thefirst threshold (206_Yes in FIG. 2), numeral 408 represents the powerconsumed for blocks 208 to 212. If the second metric level does notexceed the second threshold (i.e., insufficient energy detected) at time412, the device may return to the standby mode, indicated by intervalsof power consumption after time 412 being represented by numeral 402.

FIG. 4B illustrates another example of power consumption over time forexecuting portions of the method of FIG. 2 according to aspects of thepresent disclosure. In this example, the events of the standby modebefore time 406 are similar to that of FIG. 4A.

At time 406, assuming the sensor may have sensed a finger, the devicemay continue to perform the gradual power wake-up mechanism as describedin FIG. 2. Numeral 404 represents the power consumed for blocks 202 to206 of FIG. 2. Assuming the first metric level exceeds the firstthreshold (206_Yes in FIG. 2), numeral 410 represents the power consumedfor blocks 208 to 212. In this case, if the second metric level exceedsthe second threshold (i.e., sufficient energy detected) at time 412, thepresence of a user's finger may be detected and the device may be turnedon. Numeral 414 represents the power consumption of the device after itis turned on. After the device is activated, a full-sensor detectorconfiguration, such as the one shown in FIG. 3 (324), may be configuredto support subsequent operations, and the controller 104 may beconfigured to control the full-sensor detector configuration.

FIG. 4C illustrates exemplary implementation results of the gradualpower wake-up mechanism of FIG. 2 according to aspects of the presentdisclosure. In this exemplary implementation, one thousand data pointsare plotted of a finger touching a platen of the sensor, showing tendifferent finger touches with one hundred data points for each fingertouch. Each data point represents a metric level calculated with eitherthe 270-pixel detector configuration or the 1782-pixel detectorconfiguration and with either a finger on the sensor (plotted points 420and 424) or a finger off the sensor (plotted points 422 and 426,respectively). A threshold value (e.g. a first threshold value and asecond threshold value) used is 0.9972. As shown in FIG. 4C, plottedpoints 420 represent results of a low-power 270-pixel detectorconfiguration with a finger on the sensor; plotted points 424 representresults of an intermediate-level 1782-pixel detector configuration witha finger on the sensor; plotted points 422 represent results of thelow-power 270-pixel detector configuration with no finger on sensor; andplotted points 426 represent results of the intermediate-level1782-pixel detector configuration with no finger on the sensor. With athreshold value of 0.9972, metric levels for the 270-pixel detectorconfiguration and the 1782-pixel detector configuration are clearlydifferentiated between a finger on and a finger off the sensor, with the1782-pixel detector configuration showing smaller variation and higherseparation.

FIG. 5 illustrates a method of activating a device based on detection ofa fingerprint image according to aspects of the present disclosure. Asshown in FIG. 5, in block 502, the method may monitor a first metriclevel of a first set of regions of the fingerprint image. In block 504,the method may determine a second metric level of a second set ofregions of the fingerprint image in response to the first metric levelexceeding a first threshold. In block 506, the method may activate thedevice based on the second metric level of the second set of regions ofthe fingerprint image. In some implementations, the second set ofregions may correspond to a portion of the sensor pixels in the sensor.In some implementations, the second set of regions may correspond to theentire active area (e.g., all sensor pixels) of the sensor. Optionally,the method may monitor a third area of the fingerprint image, andconduct user interface operations with the device using the third areaof the fingerprint image. The third area may correspond to a third setof regions, which in some implementations may be the entire active areaof the sensor.

According to aspects of the present disclosure, the first metric levelmay correspond to at least one of an acoustic energy level, an acousticloading level, a spatial frequency, a cross-correlation value, an imagequality value, or some combination thereof. In some implementations, anacoustic energy level may be determined by comparing the output signalsfrom one or more sensor pixels in the first set of regions to abackground or quiescent value acquired with the ultrasonic transmitteroff (e.g., disabled), and computing the first metric level from thedifference in the output signals. In some implementations, an acousticenergy level may be determined by comparing the output signals from oneor more sensor pixels in the first set of regions to a foreground valueacquired with the ultrasonic transmitter on (e.g., enabled), andcomputing the first metric level from the difference in the outputsignals. The presence or absence of a finger on a surface of a platencoupled to the ultrasonic sensor impacts the acoustic energy level ofthe received signals. In some implementations, an acoustic loading levelmay be determined by comparing a statistical quantity such as anaverage, a weighted average, a standard deviation, etc. of the outputsignals from one or more sensor pixels in the first set of regions to abackground statistical quantity determined with the ultrasonic off or toa foreground statistical quantity determined with the ultrasonictransmitter on. The presence or absence of a finger impacts the acousticloading level. In some implementations, a spatial frequency may bedetermined from acquired output signals from a plurality of pixels inthe first set of regions by executing a fast Fourier transform (FFT) onthe acquired output signals. For example, a spatial frequency in therange of one to five line pairs per millimeter, or more closely in therange of two to three line pairs per millimeter may indicate thepresence or absence of fingerprint ridges and valleys that areindicative of the presence or absence of a finger. In someimplementations, a cross-correlation value may be determined bycomparing the output signals from a set of one or more pixels in thefirst set of regions to an adjacent set of one or more pixels in thefirst set of regions. Lack of a presence of a finger tends to result indetecting noise and/or random variations between adjacent pixels or setsof one or more pixels, whereas the presence of a finger may result insignificant signal differences between adjacent pixels or sets of one ormore pixels due to ridges and valleys of a finger or other texture of anobject positioned against the platen. In some implementations, an imagequality value may be determined from acquired output signals from one ormore pixels in the first set of regions. For example, an image qualityvalue may correspond to a contrast ratio between regions that mayrepresent a ridge of a finger and regions that may represent a valley ofthe finger. In another example, an image quality value may correspond tothe rate at which pixel output signals change from one pixel to the nextor one group of pixels to the next, indicating good feature definition.

In some implementations, more than one metric level may be combined toform a composite metric level, which may provide a better determinationof the presence of a finger. In some implementations, the second metriclevel may be determined in a manner similar to the determination of thefirst metric level. In some implementations, the second metric level mayhave similar threshold values to the first metric level; while in otherimplementations, the second metric level may have a higher thresholdvalue.

According to aspects of the present disclosure, the first set of regionsmay correspond to sensor pixels selected from one of a set of lines(e.g., a set of rows), a set of partial lines, a set of columns, a setof partial columns, a set of blocks, a set of sub-blocks, a set ofseparated pixels, a continuous line, a continuous partial line, acontinuous column, a continuous partial column, a continuous block, acontinuous sub-block, a set of continuous regions, a set ofdiscontinuous regions, or some combination thereof. The first set ofregions may be centered on an active area of an ultrasonic sensor array.In some implementations, the first set of regions may be centered on theactive area to preferentially detect a finger that is positioned overthe active area and to reduce detecting a finger that is positioned onlyover an edge of the active area.

In some implementations, the second set of regions may correspond tosensor pixels selected from one of a set of lines (e.g., a set of rows),a set of partial lines, a set of columns, a set of partial columns, aset of blocks, a set of sub-blocks, a set of separated pixels, acontinuous line, a continuous partial line, a continuous column, acontinuous partial column, a continuous block, a continuous sub-block, aset of continuous regions, a set of discontinuous regions, the entireactive area, or some combination thereof. The second set of regions maybe centered on the active area of the ultrasonic sensor array. Thesecond set of regions generally includes more pixels than the first setof regions. A block or sub-block of sensor pixels may include arectangular array of pixels with two or more adjacent pixels in a firstdirection within the array of pixels and two or more adjacent pixels ina second direction that is perpendicular to the first direction.

FIG. 6A illustrates a method of monitoring a first metric level of afirst set of regions of the fingerprint image as shown in block 502 ofFIG. 5 according to aspects of the present disclosure. In the exampleshown in FIG. 6A, in block 602, the method may receive first sampleddata of the first set of regions of a fingerprint image at a firstsampling rate. In some implementations, the first sampling rate may befive frames or partial frames per second for a sampling rate of 5 Hz.

In block 604, the method may determine the first metric level forindicating an initial prediction of a presence of a finger using thefirst sampled data. In the optional block 606, the method may monitorthe first metric level of the first set of regions of the fingerprintimage in response to the first metric level being less than or equal tothe first threshold. In some implementations, the first set of regionsof the fingerprint image may include a set of pixels arranged along aplurality of lines, where the set of pixels may include a 270-pixelpattern. The 270-pixel pattern may include five lines of 54 pixels perline, and where each line may include three line segments of 18 pixelsper line segment.

FIG. 6B illustrates a method of determining a second metric level of asecond set of regions of the fingerprint image as shown in block 504 ofFIG. 5 according to aspects of the present disclosure. As shown in FIG.6B, in block 612, the method may receive second sampled data of thesecond set of regions of the fingerprint image at a second samplingrate. The second sampling rate can be one time. In some implementations,the second sampling rate may be just once or at a frequency depending onthe size of the fingerprint image being monitored, resolution, powerconsumption, and/or other factors. In some implementations, the secondsampling rate may be equal to or faster than the first sampling rate. Inblock 614, the method may determine the second metric level forindicating a finer prediction of a presence of a finger using the secondsampled data. In some implementations, the second set of regions of thefingerprint image may include a set of pixels arranged in a plurality ofclusters, where the set of pixels may include a 1782-pixel pattern. Insome implementations, the 1782-pixel pattern may include threesub-blocks of pixels, with each sub-block having a size of 18 pixels by33 pixels.

FIG. 6C illustrates a method of activating the device based on thesecond metric level of the second set of regions of the fingerprintimage as shown in block 506 of FIG. 5 according to aspects of thepresent disclosure. In the embodiment of FIG. 6C, in block 622, themethod may determine a presence of a finger in response to the secondmetric level exceeding a second threshold. In block 624, the method mayactivate the device in response to the presence of the finger beingdetermined. In the optional block 626, the method may monitor the firstmetric level of the first set of regions of the fingerprint image inresponse to the second metric level being less than or equal to thesecond threshold.

FIG. 6D illustrates an exemplary method of determining a metric levelfor a set of regions of the fingerprint image according to aspects ofthe present disclosure. In the embodiment of FIG. 6D, in block 632, themethod may determine a change in foreground based on a presence of thefingerprint image. In block 634, the method may perform a backgroundestimation for the first set of regions of the fingerprint image. Inblock 636, the method may determine the first metric level of the firstset of regions based on differences between the change in foreground andthe background estimation for the first set of regions of thefingerprint image.

FIG. 6E illustrates an exemplary method of determining the change inforeground for a set of regions of the fingerprint image according toaspects of the present disclosure. In the embodiment of FIG. 6E, inblock 642, the method may receive first sampled foreground data in thefirst set of sampled data, where the first sampled foreground data iscollected with an ultrasonic transmitter in an enabled state (alsoreferred to as an ON state). In block 644, the method may receive secondsampled foreground data in the first set of sampled data, where thesecond sampled foreground data is collected with the ultrasonictransmitter in a disabled state (also referred to as an OFF state). Inblock 646, the method may compute the change in foreground for the setof regions of the fingerprint image as a difference between the firstsampled foreground data and the second sampled foreground data. Notethat the difference between the first sampled foreground data and thesecond sampled foreground data may be configured to reduce signals dueto a pyroelectric effect introduced when a finger/object touches or ispositioned near a piezoelectric layer of an ultrasonic sensor. Apyroelectric effect may be caused by the ability of certain materialssuch as piezoelectric materials to generate a temporary voltage when thematerials are heated or cooled. The change in temperature modifies thepositions of the atoms slightly within the crystal structure, such thatthe polarization of the material changes. This polarization change givesrise to a surface charge on a surface of the pyroelectric material andgenerates a voltage across the crystal. If the temperature staysconstant at its new value, the pyroelectric voltage gradually disappearsdue to charge leakage. The leakage can be due to electrons movingthrough the crystal, ions moving through the air, current leakingthrough a voltmeter attached across the crystal, etc. By reducing orcanceling out the pyroelectric effect, a more accurate ultrasonic signalcan be obtained.

FIG. 6F illustrates an exemplary method of performing backgroundestimation according to aspects of the present disclosure. In theembodiment of FIG. 6F, in block 652, the method may determine an updatedacquisition time delay and an updated ultrasonic transmitter frequencyin accordance with a variation of a current temperature relative to areference temperature from which an initial background estimation and aninitial ultrasonic transmitter frequency may be determined. In block654, the method may acquire background image information based on theupdated acquisition time delay and the updated ultrasonic transmitterfrequency. In block 656, the method may compute the backgroundestimation using the background image information.

Optionally or additionally, the method may perform at least one of:reduce background noise based on autocorrelation of the pixels in theset of regions (block 658); reduce sensor artifacts by removingquiescent values in the sampled data (block 660); or a combinationthereof. In one implementation, autocorrelation of the pixels in the setof regions may be performed with a shift or lag of one pixel in thehorizontal direction in the fingerprint image shown in FIG. 3.

Note that the methods described in FIG. 6D to FIG. 6F may be employed todetermine the first metric level for indicating an initial prediction ofa presence of a finger using the first sampled data as well as todetermine the second metric level for indicating a finer prediction of apresence of a finger using the second sampled data.

FIG. 7 illustrates an exemplary block diagram of a device that may beconfigured to implement the gradual power wake-up mechanism according toaspects of the present disclosure. A device that may implement thegradual power wake-up mechanism may include one or more features ofmobile device 700 shown in FIG. 7. In certain embodiments, mobile device700 may include a wireless transceiver 721 that is capable oftransmitting and receiving wireless signals 723 via wireless antenna 722over a wireless communication network. Wireless transceiver 721 may beconnected to bus 701 by a wireless transceiver bus interface 720.Wireless transceiver bus interface 720 may, in some embodiments be atleast partially integrated with wireless transceiver 721. Someembodiments may include multiple wireless transceivers 721 and wirelessantennas 722 to enable transmitting and/or receiving signals accordingto a corresponding multiple wireless communication standards such as,for example, versions of IEEE Std. 802.11, CDMA, WCDMA, LTE, UMTS, GSM,AMPS, Zigbee and Bluetooth®, etc.

Mobile device 700 may also include GPS receiver 755 capable of receivingand acquiring GPS signals 759 via GPS antenna 758. GPS receiver 755 mayalso process, in whole or in part, acquired GPS signals 759 forestimating a location of a mobile device. In some embodiments,processor(s) 711, memory 740, DSP(s) 712 and/or specialized processors(not shown) may also be utilized to process acquired GPS signals, inwhole or in part, and/or calculate an estimated location of mobiledevice 700, in conjunction with GPS receiver 755. Storage of GPS orother signals may be performed in memory 740 or registers (not shown).

Also shown in FIG. 7, mobile device 700 may include digital signalprocessor(s) (DSP(s)) 712 connected to the bus 701 by a bus interface710, processor(s) 711 connected to the bus 701 by a bus interface 710and memory 740. Bus interface 710 may be integrated with the DSP(s) 712,processor(s) 711 and memory 740. In various embodiments, functions maybe performed in response execution of one or more machine-readableinstructions stored in memory 740 such as on a computer-readable storagemedium, such as RAM, ROM, FLASH, or disc drive, just to name a fewexamples. The one or more instructions may be executable by processor(s)711, specialized processors, or DSP(s) 712. Memory 740 may include anon-transitory processor-readable memory and/or a computer-readablememory that stores software code (programming code, instructions, etc.)that are executable by processor(s) 711 and/or DSP(s) 712 to performfunctions described herein. In a particular implementation, wirelesstransceiver 721 may communicate with processor(s) 711 and/or DSP(s) 712through bus 701 to enable mobile device 700 to be configured as awireless station. Processor(s) 711 and/or DSP(s) 712 may perform themethods and functions, and execute instructions to execute one or moreaspects of processes/methods discussed in connection with FIG. 1 to FIG.6F and FIG. 8 to FIG. 9B.

Also shown in FIG. 7, a user interface 735 may include any one ofseveral devices such as, for example, a speaker, microphone, displaydevice, vibration device, keyboard, touch screen, etc. A user interfacesignal provided to a user may be one or more outputs provided by any ofthe speaker, microphone, display device, vibration device, keyboard,touch screen, etc. In a particular implementation, user interface 735may enable a user to interact with one or more applications hosted onmobile device 700. For example, devices of user interface 735 may storeanalog or digital signals on memory 740 to be further processed byDSP(s) 712 or processor 711 in response to action from a user.Similarly, applications hosted on mobile device 700 may store analog ordigital signals on memory 740 to present an output signal to a user. Inanother implementation, mobile device 700 may optionally include adedicated audio input/output (I/O) device 770 comprising, for example, adedicated speaker, microphone, digital to analog circuitry, analog todigital circuitry, amplifiers and/or gain control. In anotherimplementation, mobile device 700 may include touch sensors 762responsive to touching, pressure, or ultrasonic signals on a keyboard ortouch screen device.

Mobile device 700 may also include a dedicated camera device 764 forcapturing still or moving imagery. Dedicated camera device 764 mayinclude, for example an imaging sensor (e.g., charge coupled device orCMOS imager), lens, analog to digital circuitry, frame buffers, etc. Inone implementation, additional processing, conditioning, encoding orcompression of signals representing captured images may be performed atprocessor 711 or DSP(s) 712. Alternatively, a dedicated video processor768 may perform conditioning, encoding, compression or manipulation ofsignals representing captured images. Additionally, dedicated videoprocessor 768 may decode/decompress stored image data for presentationon a display device (not shown) on mobile device 700.

Mobile device 700 may also include sensors 760 coupled to bus 701 whichmay include, for example, inertial sensors and environmental sensors.Inertial sensors of sensors 760 may include, for example accelerometers(e.g., collectively responding to acceleration of mobile device 700 inthree dimensions), one or more gyroscopes or one or more magnetometers(e.g., to support one or more compass applications). Environmentalsensors of mobile device 700 may include, for example, temperaturesensors, barometric pressure sensors, ambient light sensors, and cameraimagers, microphones, just to name few examples. Sensors 760 maygenerate analog or digital signals that may be stored in memory 740 andprocessed by DPS(s) or processor 711 in support of one or moreapplications such as, for example, applications directed to positioningor navigation operations.

In a particular implementation, mobile device 700 may include adedicated modem processor 766 capable of performing baseband processingof signals received and down-converted at wireless transceiver 721 orGPS receiver 755. Similarly, dedicated modem processor 766 may performbaseband processing of signals to be up-converted for transmission bywireless transceiver 721. In alternative implementations, instead ofhaving a dedicated modem processor, baseband processing may be performedby a processor or DSP (e.g., processor 711 or DSP(s) 712).

FIGS. 8A-8C illustrate an example of an ultrasonic sensor according toaspects of the present disclosure. As shown in FIG. 8A, an ultrasonicsensor 10 may include an ultrasonic transmitter 20 and an ultrasonicreceiver 30 under a platen 40. The ultrasonic transmitter 20 may be apiezoelectric transmitter that can generate ultrasonic waves 21 (seeFIG. 8B). The ultrasonic receiver 30 may include a piezoelectricmaterial and an array of pixel circuits disposed in or on a substrate.In some implementations, the substrate may be a glass, plastic orsemiconductor substrate such as a silicon substrate. In operation, theultrasonic transmitter 20 may generate one or more ultrasonic waves thattravel through the ultrasonic receiver 30 to the exposed surface 42 ofthe platen 40. At the exposed surface 42 of the platen 40, theultrasonic energy may be transmitted, absorbed or scattered by an object25 that is in contact with the platen 40, such as the skin of afingerprint ridge 28, or reflected back. In those locations where aircontacts the exposed surface 42 of the platen 40, e.g., valleys 27between fingerprint ridges 28, most of the ultrasonic wave will bereflected back toward the ultrasonic receiver 30 for detection (see FIG.8C). Control electronics 50 may be coupled to the ultrasonic transmitter20 and ultrasonic receiver 30 and may supply timing signals that causethe ultrasonic transmitter 20 to generate one or more ultrasonic waves21. The control electronics 50 may then receive signals from theultrasonic receiver 30 that are indicative of reflected ultrasonicenergy 23. The control electronics 50 may use output signals receivedfrom the ultrasonic receiver 30 to construct a digital image of theobject 25. In some implementations, the control electronics 50 may also,over time, successively sample the output signals to detect the presenceand/or movement of the object 25.

According to aspects of the present disclosure, the ultrasonictransmitter 20 may be a plane wave generator including a substantiallyplanar piezoelectric transmitter layer. Ultrasonic waves may begenerated by applying a voltage to the piezoelectric layer to expand orcontract the layer, depending upon the signal applied, therebygenerating a plane wave. The voltage may be applied to the piezoelectrictransmitter layer via a first transmitter electrode and a secondtransmitter electrode. In this fashion, an ultrasonic wave may be madeby changing the thickness of the layer via a piezoelectric effect. Thisultrasonic wave travels toward a finger (or other object to bedetected), passing through the platen 40. A portion of the wave notabsorbed or transmitted by the object to be detected may be reflected soas to pass back through the platen 40 and be received by the ultrasonicreceiver 30. The first and second transmitter electrodes may bemetallized electrodes, for example, metal layers that coat opposingsides of the piezoelectric transmitter layer.

The ultrasonic receiver 30 may include an array of pixel circuitsdisposed in or on a substrate, which also may be referred to as a waferor a backplane, and a piezoelectric receiver layer. In someimplementations, each pixel circuit may include one or more silicon orthin-film transistor (TFT) elements, electrical interconnect traces and,in some implementations, one or more additional circuit elements such asdiodes, capacitors, and the like. Each pixel circuit may be configuredto convert an electric charge generated in the piezoelectric receiverlayer proximate to the pixel circuit into an electrical signal. Eachpixel circuit may include a pixel input electrode that electricallycouples the piezoelectric receiver layer to the pixel circuit.

In the illustrated implementation, a receiver bias electrode is disposedon a side of the piezoelectric receiver layer proximal to platen 40. Thereceiver bias electrode may be a metallized electrode and may begrounded or biased to control which signals are passed to the silicon orTFT sensor array. Ultrasonic energy that is reflected from the exposed(top) surface 42 of the platen 40 is converted into localized electricalcharges by the piezoelectric receiver layer. These localized charges arecollected by the pixel input electrodes and are passed on to theunderlying pixel circuits. The charges may be amplified by the pixelcircuits and provided to the control electronics, which processes theoutput signals. A simplified schematic of an example pixel circuit isshown in FIG. 9A, however one of ordinary skill in the art willappreciate that many variations of and modifications to the examplepixel circuit shown in the simplified schematic may be contemplated.

Control electronics 50 may be electrically connected to the firsttransmitter electrode and the second transmitter electrode, as well asto the receiver bias electrode and the pixel circuits in or on thesubstrate. The control electronics 50 may operate substantially asdiscussed previously with respect to FIGS. 8A-8C.

The platen 40 may be any appropriate material that can be acousticallycoupled to the receiver, with examples including plastic, ceramic,glass, sapphire, stainless steel, aluminum, a metal, a metal alloy,polycarbonate, a polymeric material, or a metal-filled plastic. In someimplementations, the platen 40 may be a cover plate, e.g., a cover glassor a lens glass for a display device or an ultrasonic sensor. Detectionand imaging may be performed through relatively thick platens ifdesired, e.g., 3 mm and above.

Examples of piezoelectric materials that may be employed according tovarious implementations include piezoelectric polymers havingappropriate acoustic properties, for example, acoustic impedance betweenabout 2.5 MRayls and 5 MRayls. Specific examples of piezoelectricmaterials that may be employed include ferroelectric polymers such aspolyvinylidene fluoride (PVDF) and polyvinylidenefluoride-trifluoroethylene (PVDF-TrFE) copolymers. Examples of PVDFcopolymers include 60:40 (molar percent) PVDF-TrFE, 70:30 PVDF-TrFE,80:20 PVDF-TrFE, and 90:10 PVDR-TrFE. Other examples of piezoelectricmaterials that may be employed include polyvinylidene chloride (PVDC)homopolymers and copolymers, polytetrafluoroethylene (PTFE) homopolymersand copolymers, and diisopropylammonium bromide (DIPAB).

The thickness of each of the piezoelectric transmitter layer and thepiezoelectric receiver layer may be selected so as to be suitable forgenerating and receiving ultrasonic waves. In one example, a PVDFpiezoelectric transmitter layer may be approximately 28 μm thick and aPVDF-TrFE receiver layer may be approximately 12 μm thick. Examplefrequencies of the ultrasonic waves are in the range of 5 MHz to 30 MHz,with wavelengths on the order of a quarter of a millimeter or less.

FIGS. 8A-8C show example arrangements of ultrasonic transmitters andreceivers in an ultrasonic sensor, with other arrangements possible. Forexample, in some implementations, the ultrasonic transmitter 20 may beabove the ultrasonic receiver 30, i.e., closer to the object ofdetection. In some implementations, the piezoelectric receiver layer mayserve as both an ultrasonic transmitter and an ultrasonic receiver. Apiezoelectric layer that may serve as either an ultrasonic transmitteror an ultrasonic receiver may be referred to as a piezoelectrictransceiver layer or as a single-layer transmitter/receiver layer. Insome implementations, the ultrasonic sensor may include an acousticdelay layer. For example, an acoustic delay layer may be incorporatedinto the ultrasonic sensor 10 between the ultrasonic transmitter 20 andthe ultrasonic receiver 30. An acoustic delay layer may be employed toadjust the ultrasonic pulse timing, and at the same time electricallyinsulate the ultrasonic receiver 30 from the ultrasonic transmitter 20.The delay layer may have a substantially uniform thickness, with thematerial used for the delay layer and/or the thickness of the delaylayer selected to provide a desired delay in the time for reflectedultrasonic energy to reach the ultrasonic receiver 30. In doing so, therange of time during which an energy pulse that carries informationabout the object by virtue of having been reflected by the object may bemade to arrive at the ultrasonic receiver 30 during a time range when itis unlikely that energy reflected from other parts of the ultrasonicsensor 10 is arriving at the ultrasonic receiver 30. In someimplementations, the silicon or TFT substrate and/or the platen 40 mayserve as an acoustic delay layer.

FIG. 9A depicts a 4×4 pixel array of pixels for an ultrasonic sensor.Each pixel may, for example, be associated with a local region ofpiezoelectric sensor material, a peak detection diode and a readouttransistor; many or all of these elements may be formed on or in thebackplane to form the pixel circuit. In practice, the local region ofpiezoelectric sensor material of each pixel may transduce receivedultrasonic energy into electrical charges. The peak detection diode mayregister the maximum amount of charge detected by the local region ofpiezoelectric sensor material. Each row of the pixel array may then bescanned, e.g., through a row select mechanism, a gate driver, or a shiftregister, and the readout transistor for each column may be triggered toallow the magnitude of the peak charge for each pixel to be read byadditional circuitry, e.g., a multiplexer and an A/D converter. Thepixel circuit may include one or more silicon transistors or TFTs toallow gating, addressing, and resetting of the pixel.

Each pixel circuit may provide information about a small portion of theobject detected by the ultrasonic sensor 10. While, for convenience ofillustration, the example shown in FIG. 9A is of a relatively coarseresolution, ultrasonic sensors having a resolution on the order of 500pixels per inch or higher may be configured with a layered structure.The detection area of the ultrasonic sensor 10 may be selected dependingon the intended object of detection. For example, the detection area(e.g., active area) may range from about 5 mm×5 mm for a single fingerto about 3 inches×3 inches for four fingers. Smaller and larger areas,including square, rectangular and non-rectangular geometries, may beused as appropriate for the object.

FIG. 9B shows an example of a high-level block diagram of an ultrasonicsensor system. Many of the elements shown may form part of controlelectronics 50. A sensor controller may include a control unit that isconfigured to control various aspects of the sensor system, e.g.,ultrasonic transmitter timing and excitation waveforms, bias voltagesfor the ultrasonic receiver and pixel circuitry, pixel addressing,signal filtering and conversion, readout frame rates, and so forth. Thesensor controller may also include a data processor that receives datafrom the ultrasonic sensor circuit pixel array. The data processor maytranslate the digitized data into image data of a fingerprint or formatthe data for further processing.

For example, the control unit may send a transmitter (Tx) excitationsignal to a Tx driver at regular intervals to cause the Tx driver toexcite the ultrasonic transmitter and produce planar ultrasonic waves.The control unit may send level select input signals through a receiver(Rx) bias driver to bias the receiver bias electrode and allow gating ofacoustic signal detection by the pixel circuitry. A demultiplexer may beused to turn on and off gate drivers that cause a particular row orcolumn of sensor pixel circuits to provide sensor output signals. Outputsignals from the pixels may be sent through a charge amplifier, a filtersuch as an RC filter or an anti-aliasing filter, and a digitizer to thedata processor. Note that portions of the system may be included on thesilicon or TFT substrate and other portions may be included in anassociated integrated circuit (e.g., an ASIC).

According to aspects of the present disclosure, ultrasonic sensors maybe configured to produce high-resolution fingerprint images for userverification and authentication. In some implementations, ultrasonicfingerprint sensors may be configured to detect reflected signalsproportional to the differential acoustic impedance between an outersurface of a platen and a finger ridge (tissue) and valley (air). Forexample, a portion of the ultrasonic wave energy of an ultrasonic wavemay be transmitted from the sensor into finger tissue in the ridge areaswhile the remaining portion of the ultrasonic wave energy is reflectedback towards the sensor, whereas a smaller portion of the wave may betransmitted into the air in the valley regions of the finger while theremaining portion of the ultrasonic wave energy is reflected back to thesensor. Methods of correcting diffraction effects disclosed herein mayincrease the overall signal and image contrast from the sensor.

Note that at least the following three paragraphs, FIG. 1-FIG. 2, FIG.5-FIG. 9 and their corresponding descriptions provide means formonitoring a first metric level of a first set of regions of thefingerprint image; means for determining a second metric level of asecond set of regions of the fingerprint image in response to the firstmetric level exceeding a first threshold, where the second set ofregions includes more pixels than the first set of regions; means foractivating the device based on the second metric level of the second setof regions of the fingerprint image; means for receiving first sampleddata from the first set of regions of the fingerprint image at a firstsampling rate; means for determining the first metric level forindicating an initial prediction of a presence of a finger using thefirst sampled data; means for receiving second sampled data from thesecond set of regions of the fingerprint image; means for determiningthe second metric level for indicating a finer prediction of a presenceof a finger using the second sampled data; means for determining apresence of a finger in response to the second metric level exceeding asecond threshold; means for activating the device in response to thepresence of the finger; means for determining a change in foregroundbased on a presence of the fingerprint image; means for performing abackground estimation for the first set of regions of the fingerprintimage; and means for determining the first metric level of the first setof regions based on differences between the change in foreground and thebackground estimation for the first set of regions of the fingerprintimage.

The methodologies described herein may be implemented by various meansdepending upon applications according to particular examples. Forexample, such methodologies may be implemented in hardware, firmware,software, or combinations thereof. In a hardware implementation, forexample, a processing unit may be implemented within one or moreapplication specific integrated circuits (“ASICs”), digital signalprocessors (“DSPs”), digital signal processing devices (“DSPDs”),programmable logic devices (“PLDs”), field programmable gate arrays(“FPGAs”), processors, controllers, micro-controllers, microprocessors,electronic devices, other devices units designed to perform thefunctions described herein, or combinations thereof.

Some portions of the detailed description included herein are presentedin terms of algorithms or symbolic representations of operations onbinary digital signals stored within a memory of a specific apparatus orspecial purpose computing device or platform. In the context of thisparticular specification, the term specific apparatus or the likeincludes a general purpose computer once it is programmed to performparticular operations pursuant to instructions from program software.Algorithmic descriptions or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processing orrelated arts to convey the substance of their work to others skilled inthe art. An algorithm is here, and generally, is considered to be aself-consistent sequence of operations or similar signal processingleading to a desired result. In this context, operations or processinginvolve physical manipulation of physical quantities. Typically,although not necessarily, such quantities may take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared or otherwise manipulated. It has proven convenient attimes, principally for reasons of common usage, to refer to such signalsas bits, data, values, elements, symbols, characters, terms, numbers,numerals, or the like. It should be understood, however, that all ofthese or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the discussion herein, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining” or the likerefer to actions or processes of a specific apparatus, such as a specialpurpose computer, special purpose computing apparatus or a similarspecial purpose electronic computing device. In the context of thisspecification, therefore, a special purpose computer or a similarspecial purpose electronic computing device is capable of manipulatingor transforming signals, typically represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of the specialpurpose computer or similar special purpose electronic computing device.

Wireless communication techniques described herein may be in connectionwith various wireless communications networks such as a wireless widearea network (“WWAN”), a wireless local area network (“WLAN”), awireless personal area network (WPAN), and so on. The term “network” and“system” may be used interchangeably herein. A WWAN may be a CodeDivision Multiple Access (“CDMA”) network, a Time Division MultipleAccess (“TDMA”) network, a Frequency Division Multiple Access (“FDMA”)network, an Orthogonal Frequency Division Multiple Access (“OFDMA”)network, a Single-Carrier Frequency Division Multiple Access (“SC-FDMA”)network, or any combination of the above networks, and so on. A CDMAnetwork may implement one or more radio access technologies (“RATs”)such as cdma2000, Wideband-CDMA (“W-CDMA”), to name just a few radiotechnologies. Here, cdma2000 may include technologies implementedaccording to IS-95, IS-2000, and IS-856 standards. A TDMA network mayimplement Global System for Mobile Communications (“GSM”), DigitalAdvanced Mobile Phone System (“D-AMPS”), or some other RAT. GSM andW-CDMA are described in documents from a consortium named “3rdGeneration Partnership Project” (“3GPP”). Cdma2000 is described indocuments from a consortium named “3rd Generation Partnership Project 2”(“3GPP2”). 3GPP and 3GPP2 documents are publicly available. 4G Long TermEvolution (“LTE”) communications networks may also be implemented inaccordance with claimed subject matter, in an aspect. A WLAN may includean IEEE 802.11x network, and a WPAN may include a Bluetooth network, anIEEE 802.15x, for example. Wireless communication implementationsdescribed herein may also be used in connection with any combination ofWWAN, WLAN or WPAN.

In another aspect, as previously mentioned, a wireless transmitter oraccess point may include a femtocell, utilized to extend cellulartelephone service into a business or home. In such an implementation,one or more mobile devices may communicate with a femtocell via a codedivision multiple access (“CDMA”) cellular communication protocol, forexample, and the femtocell may provide the mobile device access to alarger cellular telecommunication network by way of another broadbandnetwork such as the Internet.

The terms, “and,” and “or” as used herein may include a variety ofmeanings that will depend at least in part upon the context in which itis used. Typically, “or” if used to associate a list, such as A, B or C,is intended to mean A, B, and C, here used in the inclusive sense, aswell as A, B or C, here used in the exclusive sense. Referencethroughout this specification to “one example” or “an example” meansthat a particular feature, structure, or characteristic described inconnection with the example is included in at least one example ofclaimed subject matter. Thus, the appearances of the phrase “in oneexample” or “an example” in various places throughout this specificationare not necessarily all referring to the same example. Furthermore, theparticular features, structures, or characteristics may be combined inone or more examples. Examples described herein may include machines,devices, engines, or apparatuses that operate using digital signals.Such signals may include electronic signals, optical signals,electromagnetic signals, or any form of energy that provides informationbetween locations.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein. Therefore, it isintended that claimed subject matter not be limited to the particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of the appended claims, andequivalents thereof.

We claim:
 1. A method for use in activating a device based on detectionof a fingerprint image, the method comprising: monitoring a first metriclevel of a first set of regions of a fingerprint image; determining asecond metric level of a second set of regions of the fingerprint imagein response to the first metric level exceeding a first threshold,wherein the second set of regions includes more pixels than the firstset of regions; and activating the device based on the second metriclevel of the second set of regions of the fingerprint image.
 2. Themethod of claim 1, wherein the first metric level and the second metriclevel represent at least one of an acoustic energy level, an acousticloading level, a spatial frequency, a cross-correlation value, or animage quality value.
 3. The method of claim 1, wherein monitoring thefirst metric level of the first set of regions of the fingerprint imagecomprises: receiving a first sampled data from the first set of regionsof the fingerprint image at a first sampling rate; and determining thefirst metric level for indicating an initial prediction of a presence ofa finger using the first sampled data.
 4. The method of claim 3, furthercomprising: monitoring the first metric level of the first set ofregions of the fingerprint image in response to the first metric levelbeing less than or equal to the first threshold.
 5. The method of claim1, wherein determining the second metric level of the second set ofregions of the fingerprint image comprises: receiving a second sampleddata from the second set of regions of the fingerprint image; anddetermining the second metric level for indicating a finer prediction ofa presence of a finger using the second sampled data.
 6. The method ofclaim 1, wherein activating the device based on the second metric levelof the second set of regions of the fingerprint image comprises:determining a presence of a finger in response to the second metriclevel exceeding a second threshold; and activating the device inresponse to the presence of the finger.
 7. The method of claim 6,further comprising: monitoring the first metric level of the first setof regions of the fingerprint image in response to the second metriclevel being less than or equal to the second threshold.
 8. The method ofclaim 3, wherein determining the first metric level of the first set ofregions of the fingerprint image comprises: determining a change inforeground based on a presence of the fingerprint image; performing abackground estimation for the first set of regions of the fingerprintimage; and determining the first metric level of the first set ofregions based on differences between the change in foreground and thebackground estimation for the first set of regions of the fingerprintimage.
 9. The method of claim 8, wherein determining the change inforeground comprises: receiving a first sampled foreground data in thefirst set of sampled data, wherein the first sampled foreground data iscollected with an ultrasonic transmitter in an enabled state; receivinga second sampled foreground data in the first set of sampled data,wherein the second sampled foreground data is collected with theultrasonic transmitter in a disabled state; and computing the change inforeground for the first set of regions of the fingerprint image as adifference between the first sampled foreground data and the secondsampled foreground data.
 10. The method of claim 8, wherein performingthe background estimation comprises: determining an updated acquisitiontime delay and an updated ultrasonic transmitter frequency in accordancewith a variation of a current temperature from a reference temperaturefrom which an initial background estimation and an initial ultrasonictransmitter frequency are determined; acquiring a background imageinformation based on the updated acquisition time delay and the updatedultrasonic transmitter frequency; and computing the backgroundestimation using the background image information.
 11. The method ofclaim 10, further comprising at least one of: reducing background noisebased on autocorrelation of the pixels in the first set of regions;reducing sensor artifacts by removing quiescent values in the firstsampled data; or a combination thereof.
 12. A device, comprising: asensor having a plurality of sensor pixels configured to sense afingerprint image; a memory configured to store the fingerprint image;and a controller coupled to the sensor and the memory and configured to:monitor a first metric level of a first set of regions of thefingerprint image; determine a second metric level of a second set ofregions of the fingerprint image in response to the first metric levelexceeding a first threshold, wherein the second set of regions includesmore pixels than the first set of regions; and activate the device basedon the second metric level of the second set of regions of thefingerprint image.
 13. The device of claim 12, wherein the first metriclevel and the second metric level represent at least one of an acousticenergy level, an acoustic loading level, a spatial frequency, across-correlation value, or an image quality value.
 14. The device ofclaim 12, wherein the controller is further configured to: receive afirst sampled data from the first set of regions of the fingerprintimage at a first sampling rate; and determine the first metric level forindicating an initial prediction of a presence of a finger using thefirst sampled data.
 15. The device of claim 14, wherein the controlleris further configured to: monitor the first metric level of the firstset of regions of the fingerprint image in response to the first metriclevel being less than or equal to the first threshold.
 16. The device ofclaim 12, wherein the controller is further configured to: receive asecond sampled data from the second set of regions of the fingerprintimage; and determine the second metric level for indicating a finerprediction of a presence of a finger using the second sampled data. 17.The device of claim 12, wherein the controller is further configured to:determine a presence of a finger in response to the second metric levelexceeding a second threshold; and activate the device in response to thepresence of the finger.
 18. The device of claim 17, wherein thecontroller is further configured to: monitor the first metric level ofthe first set of regions of the fingerprint image in response to thesecond metric level being less than or equal to the second threshold.19. The device of claim 14, wherein the controller is further configuredto: determine a change in foreground based on a presence of thefingerprint image; perform a background estimation for the first set ofregions of the fingerprint image; and determine the first metric levelof the first set of regions based on differences between the change inforeground and the background estimation for the first set of regions ofthe fingerprint image.
 20. The device of claim 19, wherein thecontroller is further configured to: receive a first sampled foregrounddata in the first set of sampled data, wherein the first sampledforeground data is collected with an ultrasonic transmitter in anenabled state; receive a second sampled foreground data in the first setof sampled data, wherein the second sampled foreground data is collectedwith the ultrasonic transmitter in a disabled state; and compute thechange in foreground for the first set of regions of the fingerprintimage as a difference between the first sampled foreground data and thesecond sampled foreground data.
 21. The device of claim 19, wherein thecontroller is further configured to: determine an updated acquisitiontime delay and an updated ultrasonic transmitter frequency in accordancewith a variation of a current temperature from a reference temperaturefrom which an initial background estimation and an initial ultrasonictransmitter frequency are determined; acquire a background imageinformation based on the updated acquisition time delay and the updatedultrasonic transmitter frequency; and compute the background estimationusing the background image information.
 22. The device of claim 21,wherein the controller is further configured to perform at least one of:reduce background noise based on autocorrelation of the pixels in thefirst set of regions; reduce sensor artifacts by removing quiescentvalues in the first sampled data; or a combination thereof.
 23. Anon-transitory medium storing instructions for execution by one or moreprocessors of a device, the instructions comprising: instructions formonitoring a first metric level of a first set of regions of afingerprint image; instructions for determining a second metric level ofa second set of regions of the fingerprint image in response to thefirst metric level exceeding a first threshold, wherein the second setof regions includes more pixels than the first set of regions; andinstructions for activating the device based on the second metric levelof the second set of regions of the fingerprint image.
 24. Thenon-transitory medium of claim 23, wherein the instructions formonitoring the first metric level of the first set of regions of thefingerprint image comprise: instructions for receiving a first sampleddata from the first set of regions of the fingerprint image at a firstsampling rate; and instructions for determining the first metric levelfor indicating an initial prediction of a presence of a finger using thefirst sampled data.
 25. The non-transitory medium of claim 23, whereinthe instructions for determining the second metric level of the secondset of regions of the fingerprint image comprise: instructions forreceiving a second sampled data from the second set of regions of thefingerprint image; and instructions for determining the second metriclevel for indicating a finer prediction of a presence of a finger usingthe second sampled data.
 26. The non-transitory medium of claim 23,wherein the instructions for activating the device based on the secondmetric level of the second set of regions of the fingerprint imagecomprise: instructions for determining a presence of a finger inresponse to the second metric level exceeding a second threshold; andinstructions for activating the device in response to the presence ofthe finger.
 27. The non-transitory medium of claim 24, wherein theinstructions for determining the first metric level of the first set ofregions of the fingerprint image comprise: instructions for determininga change in foreground based on a presence of the fingerprint image;instructions for performing a background estimation for the first set ofregions of the fingerprint image; and instructions for determining thefirst metric level of the first set of regions based on differencesbetween the change in foreground and the background estimation for thefirst set of regions of the fingerprint image.
 28. A device, comprising:means for use in sensing a fingerprint image using a plurality of sensorpixels; means for use in storing the fingerprint image; means formonitoring a first metric level of a first set of regions of thefingerprint image; means for determining a second metric level of asecond set of regions of the fingerprint image in response to the firstmetric level exceeding a first threshold, wherein the second set ofregions includes more pixels than the first set of regions; and meansfor activating the device based on the second metric level of the secondset of regions of the fingerprint image.
 29. The device of claim 28,wherein the means for monitoring the first metric level of the first setof regions of the fingerprint image comprises: means for receiving afirst sampled data from the first set of regions of the fingerprintimage at a first sampling rate; and means for determining the firstmetric level for indicating an initial prediction of a presence of afinger using the first sampled data.
 30. The device of claim 28, whereinthe means for determining the second metric level of the second set ofregions of the fingerprint image comprises: means for receiving a secondsampled data from the second set of regions of the fingerprint image;and means for determining the second metric level for indicating a finerprediction of a presence of a finger using the second sampled data.